Custom Sampler in Turing.jl

Lets say I have some Bayesian model for which the full conditional distributions of some of the variables are known in closed form. How can I write the appropriate Gibbs sampler for these variables in Turing.jl?

Lets say I’m interested in the very simple model outlined in this example: https://mambajl.readthedocs.io/en/latest/tutorial.html

I haven’t been able to find any documentation for doing this in Turing.

We have a PR open to allow this here, but it hasn’t yet been merged.

This looks PERFECT. Can’t wait for the PR merge